Package smile.regression

Class SparseLinearSVM

java.lang.Object

smile.regression.SparseLinearSVM

All Implemented Interfaces:

Serializable, ToDoubleFunction<SparseArray>, Regression<SparseArray>

public class SparseLinearSVM
extends Object
implements Regression<SparseArray>

Sparse linear support vector machines for regression.

See Also:

• Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from interface smile.regression.Regression

Regression.Trainer<T,M extends Regression<T>>

Constructor Summary

Constructors

Constructor	Description
SparseLinearSVM(int p, KernelMachine<SparseArray> svm)	Constructor.

Method Summary

Modifier and Type	Method	Description
double	intercept()	Returns the intercept.
double	predict(SparseArray x)	Predicts the dependent variable of an instance.
double[]	weights()	Returns the linear weights.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface smile.regression.Regression

applyAsDouble, online, predict, predict, predict, update, update, update

Constructor Details

SparseLinearSVM

public SparseLinearSVM(int p,
 KernelMachine<SparseArray> svm)

Constructor.

Parameters:

p - the dimension of input vector.

svm - The kernel machine.

Method Details

weights

public double[] weights()

Returns the linear weights.

Returns:

the linear weights.

intercept

public double intercept()

Returns the intercept.

Returns:

the intercept.

predict

public double predict(SparseArray x)

Description copied from interface: Regression

Predicts the dependent variable of an instance.

Specified by:

predict in interface Regression<SparseArray>

Parameters:

x - an instance.

Returns:

the predicted value of dependent variable.